Research on DDoS Attack Detection Based on Lightweight Convolutional Neural Networks
Distributed denial of service(DDoS)attacks can attack,intrude,and destroy Internet of Things devices.During the COVID-19 pe-riod,the use of a large number of IoT terminal devices for epidemic prevention and control has accelerated the frequency of information ex-change,and the overly simplistic network security defense method has also made network security issues a hot topic.Deep learning(DL)has been widely used in network security to detect and respond to various network environments with low security levels.For intelligent terminals with simple structures,traditional DL models require high computing and memory resources,and often require additional operating costs when dealing with large traffic attacks.The research proposes a model based on self attention mechanism and lightweight convolutional neural net-works(Self-attention-LCNN),which extracts features from data packets within a given time period on a stream basis to detect and prevent DDoS attacks against intelligent terminals in complex network environments.The accuracy of the Self-attention-LCNN model on the CICDDoS 2019 dataset is 99.21％.Deploying the model on a raspberry pie yielded an average detection rate of 93％,indicating that the Self-attention-LCNN model has a good recognition effect in attack detection on resource-constrained intelligent terminals.
万方数据
维普
